Paper Citation Record · LEDGER

Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting

As of 15 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2412.08099.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2412.08099 v4

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T18:18:55.188005Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

41 of 41 outbound references displayed

verified exact1
verified fuzzy10
unresolved30
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b939aec7-9f25-4fa7-909d-ed86cd5bcd58 · outbound

This paper cites GPT-4 Technical Report.

Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting GPT-4 Technical Report

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-11T18:18:54.768110Z

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Observation 425abd9b-16d4-4008-8f95-7f2ad8501432 · outbound

This paper cites Language Models are Few-Shot Learners.

Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting Language Models are Few-Shot Learners

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-11T18:18:54.776004Z

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Observation 94ffcab9-af41-407d-bba3-8dbb34f45b40 · outbound

This paper cites Nhits: Neural hierarchical interpolation for time series forecasting.

Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting Nhits: Neural hierarchical interpolation for time series forecasting

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-11T18:18:54.784178Z

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Observation a1e9f42f-4d85-4f2b-a442-f7eafd7d077f · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-11T18:18:54.797719Z

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Observation 8536bfbd-5a16-422e-b520-e581fcb37b98 · outbound

This paper cites Exponential smoothing: The state of the art.

Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting Exponential smoothing: The state of the art

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:18:56.557033Z

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Observation c12c4d3d-2dd2-4ac6-8870-7017209ea21b · outbound

This paper cites TimeGPT-1.

Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting TimeGPT-1

Reference 6

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Source-reported events for the cited work

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Observation f571311b-c5a7-42e5-b566-4df2c38e5263 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting Explaining and Harnessing Adversarial Examples

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T18:18:54.837784Z

Source-reported events for the cited work

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Observation 14de8643-1238-4f6d-8e5d-b33695843d0c · outbound

This paper cites Not what you've signed up for: Compromising real-world llm-integrated applications with indirect prompt injection.

Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting Not what you've signed up for: Compromising real-world llm-integrated applications with indirect prompt injection

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-11T18:18:54.843969Z

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Observation ff102a74-3d24-4b21-beb6-9c99f7d2cd3d · outbound

This paper cites Large language models are zero-shot time series forecasters.

Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting Large language models are zero-shot time series forecasters

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-11T18:18:54.853377Z

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Observation 593d6536-cb0a-423c-bbd5-1e339ea0f6d4 · outbound

This paper cites Gradient-based Adversarial Attacks against Text Transformers.

Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting Gradient-based Adversarial Attacks against Text Transformers

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T18:18:54.858496Z

Source-reported events for the cited work

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Observation 7a95d075-06ba-404b-a58f-480eb248909b · outbound

This paper cites Empowering Time Series Analysis with Large Language Models: A Survey.

Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting Empowering Time Series Analysis with Large Language Models: A Survey

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-11T18:18:54.863942Z

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Observation 51ba80f0-610c-4635-87e8-3b0e3c81f2c4 · outbound

This paper cites Time-LLM: Time Series Forecasting by Reprogramming Large Language Models.

Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting Time-LLM: Time Series Forecasting by Reprogramming Large Language Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T18:18:54.871390Z

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Observation 37762067-1990-4ba4-b681-209c666c9397 · outbound

This paper cites Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook.

Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T18:18:54.877978Z

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Observation 8fa9ef4e-5a68-4508-adee-0eb9cb391c74 · outbound

This paper cites Distance measures for effective clustering of arima time-series.

Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting Distance measures for effective clustering of arima time-series

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:18:56.506357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f603307b-c1dd-49a8-9740-f4fe9b8f1585 · outbound

This paper cites Modeling long-and short-term temporal patterns with deep neural networks.

Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting Modeling long-and short-term temporal patterns with deep neural networks

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:18:56.450311Z

Source-reported events for the cited work

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Observation cff67056-d779-4d01-b007-eebf73fe64a1 · outbound

This paper cites TimeCMA: Towards LLM-Empowered Multivariate Time Series Forecasting via Cross-Modality Alignment.

Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting TimeCMA: Towards LLM-Empowered Multivariate Time Series Forecasting via Cross-Modality Alignment

Reference 16

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Observation bec24694-1335-49f6-8141-ed1277c4f5cb · outbound

This paper cites Practical adversarial attacks on spatiotemporal traffic forecasting models.

Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting Practical adversarial attacks on spatiotemporal traffic forecasting models

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:18:56.394168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3ca83676-101b-4bd6-8741-26e0ca6ce0f2 · outbound

This paper cites Spatially Focused Attack against Spatiotemporal Graph Neural Networks.

Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting Spatially Focused Attack against Spatiotemporal Graph Neural Networks

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-08-11T18:18:55.737997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation cd18e936-4fd7-47be-b410-5328efcbc378 · outbound

This paper cites A universal framework of spatiotemporal bias block for long-term traffic forecasting.

Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting A universal framework of spatiotemporal bias block for long-term traffic forecasting

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:18:56.366828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4d76e26e-1f09-406f-937d-516ef1e5cede · outbound

This paper cites Adversarial danger identification on temporally dynamic graphs.

Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting Adversarial danger identification on temporally dynamic graphs

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:18:56.338538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation fdf7aa5d-1f94-4df9-b95b-7fa1551f950f · outbound

This paper cites Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms.

Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms

Reference 21

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Observation 27b9bd56-c40b-4a60-933e-4ccbff8b8d20 · outbound

This paper cites CALF: Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning.

Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting CALF: Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-11T18:18:54.977951Z

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Observation bda893b4-13e2-4b2c-86c5-a59d184a90bb · outbound

This paper cites Automatic and Universal Prompt Injection Attacks against Large Language Models.

Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting Automatic and Universal Prompt Injection Attacks against Large Language Models

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-11T18:18:54.988304Z

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Observation 7ca1c4f0-3fc0-42a3-975e-47cbfe502ebc · outbound

This paper cites itransformer: Inverted transformers are effective for time series forecasting.

Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting itransformer: Inverted transformers are effective for time series forecasting

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:18:56.309548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8e0169e7-68f1-4b3c-b75b-290ff0ed4b6e · outbound

This paper cites Towards Deep Learning Models Resistant to Adversarial Attacks.

Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-11T18:18:55.010827Z

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Observation 16c52e12-2b80-400c-849e-4ade236a231b · outbound

This paper cites TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP.

Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-11T18:18:55.028265Z

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Observation 4d9d6b33-f972-4b5b-a3fe-557ef7ea9af4 · outbound

This paper cites N-BEATS: Neural basis expansion analysis for interpretable time series forecasting.

Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting N-BEATS: Neural basis expansion analysis for interpretable time series forecasting

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-11T18:18:55.036741Z

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Observation a47bb002-7de5-44be-8a39-1cff4131b8b0 · outbound

This paper cites Deepar: Probabilistic forecasting with autoregressive recurrent networks.

Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting Deepar: Probabilistic forecasting with autoregressive recurrent networks

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-11T18:18:55.043361Z

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Observation e8e871a4-f35d-4fcd-8481-0b67b5c75348 · outbound

This paper cites Soft Prompt Threats: Attacking Safety Alignment and Unlearning in Open-Source LLMs through the Embedding Space.

Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting Soft Prompt Threats: Attacking Safety Alignment and Unlearning in Open-Source LLMs through the Embedding Space

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-11T18:18:55.053272Z

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Observation 50183753-1f36-481e-bfe9-26431a70975a · outbound

This paper cites Robustness of llms to perturbations in text.

Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting Robustness of llms to perturbations in text

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-11T18:18:55.067914Z

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Observation e0d3c555-3242-4dc5-ac88-73666bb4071d · outbound

This paper cites Are Language Models Actually Useful for Time Series Forecasting?.

Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting Are Language Models Actually Useful for Time Series Forecasting?

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-11T18:18:55.075743Z

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Observation 33086523-8ace-4e6c-85ff-e2e40574cb43 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting LLaMA: Open and Efficient Foundation Language Models

Reference 32

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Observation cf34c6e2-e72e-4c3e-a374-b191bbedf8a7 · outbound

This paper cites Jailbroken: How does llm safety training fail? Advances in Neural Information Processing Systems, 36, 2024.

Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting Jailbroken: How does llm safety training fail? Advances in Neural Information Processing Systems, 36, 2024

Reference 33

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Observation ba5e292c-2dbe-4f75-8151-bdc1e98cc034 · outbound

This paper cites Transferable Adversarial Attacks for Image and Video Object Detection.

Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting Transferable Adversarial Attacks for Image and Video Object Detection

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-11T18:18:55.098700Z

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Observation 833ad429-c433-46a4-9f20-74cb085f3486 · outbound

This paper cites Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting.

Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-11T18:18:55.111423Z

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Observation c4886d24-d965-43cc-a8c7-dba73818fcf6 · outbound

This paper cites Timesnet: Temporal 2d-variation modeling for general time series analysis.

Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting Timesnet: Temporal 2d-variation modeling for general time series analysis

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:18:56.221351Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9673e185-a723-4fe1-a9f9-005f8e4cbeae · outbound

This paper cites Adversarial attacks and defenses in images, graphs and text: A review.

Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting Adversarial attacks and defenses in images, graphs and text: A review

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:18:56.181517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b04485aa-282e-484b-a034-8b489765d259 · outbound

This paper cites Trojllm: A black-box trojan prompt attack on large language models.

Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting Trojllm: A black-box trojan prompt attack on large language models

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:18:56.153823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 030cfff3-074c-4bab-a16d-1ef9d471dd33 · outbound

This paper cites Don't Listen To Me: Understanding and Exploring Jailbreak Prompts of Large Language Models.

Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting Don't Listen To Me: Understanding and Exploring Jailbreak Prompts of Large Language Models

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-11T18:18:55.164615Z

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Observation 46e0d6ff-2ff6-4d17-b5ea-2359b9f3aea6 · outbound

This paper cites Informer: Beyond efficient transformer for long sequence time-series forecasting.

Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting Informer: Beyond efficient transformer for long sequence time-series forecasting

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-11T18:18:55.170060Z

Source-reported events for the cited work

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Observation bca939dd-bd0d-4697-adb8-89d8230c11b7 · outbound

This paper cites Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting.

Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-11T18:18:55.188005Z

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Pith citing papers

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